基于全局特征拼接的行人重识别算法研究

Person re-identification algorithm based on global feature stitching

针对目前行人重识别出现网络模型复杂化、识别率低的问题,提出一种基于全局特征拼接的行人重识别算法。首先利用卷积神经网络(CNN)提取全局特征;然后把不同卷积层提取的特征进行拼接,使特征信息互补;最后将拼接后的特征再次进行卷积处理,获得高表征能力的特征。网络训练时,采用聚类损失函数和标签平滑损失函数联合训练,同时引入了随机擦除和减小池化步长的训练技巧。在Market1501、DukeMTMC-reID、CUHK03和MSMT17数据集上进行了实验验证,实验表明所提算法具有良好性能,其中在Market1501上,Rank-1、mAP分别达到了95.9%和94.6%。

In order to solve the problems of network model complexity and low identification rate,this paper proposed a person re-identification(ReID)method based on global feature stitching.Firstly,it extracted the global features using convolutional neural network(CNN).Secondly,it stitched the features from different convolution layers together to complement the feature information.Finally,it convoluted again to obtain the features with high representation ability.In network training stage,it combined the cluster loss with label smoothing loss,and adopted random erasing augmentation(REA)as well as pooling step reduction techniques.It conducted extensive experiments to evaluate the performance of this proposed method on Market1501,DukeMTMC-reID,CUHK03 and MSMT17 benchmark datasets.Results show that the proposed method outperforms other state-of-the-art techniques,for instance,the Rank-1 and mAP on Market1501 are 95.9%and 94.6%,respectively.